Proactive, dynamic and multi-criteria scheduling of maintenance activities.

International audienceIn maintenance services skills management is directly linked to the performance of the service. A good human resource management will have an effect on the performance of the plant. Each task which has to be performed is characterised by the level of competence required. For each skill, human resources have different levels. The issue of making a decision about assignment and scheduling leads to finding the best resource and the correct time to perform the task. The solve this problem, managers have to take into account the different criteria such as the number of late tasks, the workload or the disturbance when inserting a new task into an existing planning. As there is a lot of estimated data, the managers also have to anticipate these uncertainties. To solve this multi-criteria problem, we propose a dynamic approach based on the kangaroo methodology. To deal with uncertainties, estimated data is modelled with fuzzy logic. This approach then offers the maintenance expert a choice between a set of the most robust possibilities

Ordonnancement des activités de maintenance sous contraintes de compétences.

International audienceCompetencies management in the industry is one of the most important keys in order to obtain good performance with production means. Especially in maintenance services field where the different practical knowledges or skills are their working tools. We propose here a methodology, which compares the human resource with parallel machine. As human ressource competence levels of each are all different, they are considered like unrelated parallel machines. Our aim is to assign tasks to the adequate resources by minimizing time treatment for each task and the makespan

Proposition of New Genetic Operator for Solving Joint Production and Maintenance Scheduling : Application to the Flow Shop Problem.

International audienceGenetic algorithms are used in scheduling leading to efficient heuristic methods for large sized problems. The efficiency of a GA based heuristic is closely related to the quality of the used GA scheme and the GA operators: mutation, selection and crossover. In this paper, we propose a Joint Genetic Algorithm (JGA), for joint production and maintenance scheduling problem in permutation flowshop, in which different genetic joint operators are used. We also proposed a joint structure to represent an individual in with two fields: the first one for production data and the second one for maintenance data. We used different Taillard benchmarks to compare the performances of JGA with each proposed operator

Case elaboration methodology proposed for diagnostic and repair help system based on CBR.

International audienceAlthough the elaboration of the case representation is the key problem of the case-based reasoning system conception there is no proved methodology targeted to this task for now. This paper deals with this lack in the maintenance domain precisely in the equipments diagnostic and repair help. A methodology of the case representation elaboration is proposed based on knowledge management techniques and existing engineering analytical tools used in the industry. Different ontological models are proposed to take into account similarity and adaptability aspects of the case representation and to optimize the case base size

Réseaux de neurones récurrents à fonctions de base radiales : RRFR Application au pronostic

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Réseaux de neurones récurrents à fonctions de base radiales. Application à la surveillance dynamique

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Bayesian Approach for Remaining Useful Life Prediction

Prediction of the remaining useful life (RUL) of critical components is a non-trivial task for industrial applications. RUL can differ for similar components operating under the same conditions. Working with such problem, one needs to contend with many uncertainty sources such as system, model and sensory noise. To do that, proposed models should include such uncertainties and represent the belief about the system’s state in a probabilistic form. In this work, a Bayesian approach is proposed for predicting the RUL of critical components. The approach is divided into two main parts, online and offline. In the offline part, the approach builds k-nearest neighbours classifier (kNN) for different datasets according to their end of life (EOL) values. On the other hand, the online part is similar to the offline apart from the use of Bayesian online state estimator. Bayesian online state estimator is used to represent the uncertainty of the approach about the health status. The approach starts by extracting trends that represent the health evolution of the critical component and uses these trends to build offline models of the critical component. Then, the approach uses these models to predict the RUL from new online data and assigning uncertainty value to it. The approach can be applied to a system with variable operating conditions, however, the prediction horizon will span between the minimum and maximum RUL values available in the training dataset. 1

Unsupervised trend extraction for prognostics and condition assessment

In this work, time and frequency domain features have been extracted from two sensory data series. The features have been grouped according to the quality of the relations between each of which. Then, each group of features has been projected into a compact two dimensional represenhal-00838493